In pre deep learning era, methods used hand-crafted features such as local noise analysis [1]}, [2]}, CFA artifacts [3]}, JPEG compression [4]} etc. Recent works usually use deep learning based methods in conjunction with these forgery traces to localize forged regions. Bappy et al. [5]} exploit the edge inconsistency trace using LSTM to localize forgery. The work is later improved in [6]}, where the authors further exploit resampling traces using Laplacian filters. They also use a separate encoder-decoder structure to refine the predicted mask. RGB-N [7]} proposes a two stream faster R-CNN network, one for the RGB image and other for the noise information traces generated by the Steganalysis Rich Model (SRM) filters [1]}. SRM filters are high pass filters that enhance the high-frequency information, which becomes helpful in forgery localization. However, due to the R-CNN architecture, RGB-N is limited to localizing to a rectangular box whereas real objects are not necessarily rectangular. Mantra-Net [9]} jointly detects and localizes forged images. ManTra-Net is composed of a VGG based feature extractor and an LSTM based detection module. The feature extractor is trained to detect various types of image manipulation traces. SPAN [10]} proposes Spatial Pyramid Attention Network models the relationship between image patches at multiple scales by constructing a pyramid of local self-attention blocks. CAT-Net [11]} uses two stream network similar to RGB-N, one for the RGB pixel stream and the other for DCT co-efficients. DCT helps to extract resampling trace features.
